Abstract

Deep space exploration has risen in interest among scientists in recent years, with soft landings being one of the most straightforward ways to acquire knowledge about the Moon. In general, landing mission success depends on the selection of landing zones, and there are currently few effective quantitative models that can be used to select suitable landing zones. When automatic landing zones are selected, the grid method used for data partitioning tends to miss potentially suitable landing sites between grids. Therefore, this study proposes a new engineering-constrained approach for landing zone selection using LRO LOLA-based slope data as original data based on the sliding window method, which solves the spatial omission problem of the grid method. Using the threshold ratio, mean, coefficient of variation, Moran’s I, and overall rating, this method quantifies the suitability of each sliding window. The k-means clustering algorithm is adopted to determine the suitability threshold for the overall rating. The results show that 20 of 22 lunar soft landing sites are suitable for landing. Additionally, 43 of 50 landing sites preselected by the experts (suitable landing sites considering a combination of conditions) are suitable for landing, accounting for 90.9% and 86% of the total number, respectively, for a window size of 0.5° × 0.5°. Among them, there are four soft landing sites: Surveyor 3, 6, 7, and Apollo 15, which are not suitable for landing in the evaluation results of the grid method. However, they are suitable for landing in the overall evaluation results of the sliding window method, which significantly reduces the spatial omission problem of the grid method. In addition, four candidate landing regions, including Aristarchus Crater, Marius Hills, Moscoviense Basin, and Orientale Basin, were evaluated for landing suitability using the sliding window method. The suitability of the landing area within the candidate range of small window sizes was 0.90, 0.97, 0.49, and 0.55. This indicates the capacity of the method to analyze an arbitrary range during blind landing zone selection. The results can quantify the slope suitability of the landing zones from an engineering perspective and provide different landing window options. The proposed method for selecting lunar landing zones is clearly superior to the gridding method. It enhances data processing for automatic lunar landing zone selection and progresses the selection process from qualitative to quantitative.

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